The present disclosure generally relates to information retrieval, and more specifically, automated systems that provide answers to questions or inquiries.
Generally, there are many types of information retrieval and question answering systems, including expert or knowledge-based (KB) systems, document or text search/retrieval systems and question answering (QA) systems.
Expert or knowledge-based systems take in a formal query or map natural language to a formal query and then produce a precise answer and a proof justifying the answer based on a set of formal rules encoded by humans.
Document or text search systems are not designed to deliver and justify precise answers. Rather they produce snippets or documents that contain key words or search terms entered by a user, for example, via a computing system interface, e.g., a web-browser. There is no expectation that the results provide a solution or answer. Text search systems are based on the prevailing and implicit assumption that all valid results to a query are documents or snippets that contain the keywords from the query.
QA systems provide a type of information retrieval. Given a collection of documents (such as the World Wide Web or a local collection), a QA system may retrieve answers to questions posed in natural language. QA is regarded as requiring more complex natural language processing (NLP) techniques than other types of information retrieval, such as document retrieval, and QA is sometimes regarded as the next step beyond search engines.
Traditional QA systems deliver precise answers, unlike document search systems, but do not produce paths of justifications like expert systems. Their justifications are “one-step” meaning that they provide an answer by finding one or more passages that alone suggest that proposed or candidate answer is correct.
It would be highly desirable to provide a system and method that can answer complex inquiries that search systems, classic expert/KB systems and simpler QA systems can not handle.